Reliable and Green Long-Endurance Drones

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 2209

Special Issue Editor


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Guest Editor
Dipartimento di Ingegneria Civile ed Industriale, Università di Pisa, Pisa, Italy
Interests: aerospace mechatronics; flight control systems; modelling and simulation; control design; diagnostic and prognostic condition-monitoring; experimental testing
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Special Issue Information

Dear Colleagues,

The global market for UAVs is expected to grow in the coming years despite the negative impact of the COVID-19 pandemic. Pushed by the wider objectives of aviation electrification, the design of long-endurance UAVs is moving toward the hybrid/full-electric propulsion, aiming to enhance eco-compatibility, emissions (noise, CO2 and thermal signature), efficiency, and reliability. In particular, the development of permanent magnet synchronous machines and Li-Ion batteries with high power density becomes more significant each day; conversely, at a system level, it is crucial to develop real-time executable energy management strategies to optimize the power split among available sources. In parallel, reliability/safety concerns have to be addressed, and solutions which implement fault-tolerant capabilities and efficient health-monitoring play key roles in the intergation of UAVs into unsegregated airspaces. Power optimization also involves aerodynamics, since mission and weight/envelope constraints often make it unfeasible to use conventional aerofoils and propellers. Innovative solutions (e.g., thick laminar wings, toroidal propellers) and multidisciplinary optimization can overcome these limitations. This Special Issue is thus focused on the design of reliable power-efficient drones for long-endurance applications, and we encourage submissions on, but not limited to, topics such as:

  • full-electric/hybrid propulsion;
  • high power density PMSM and batteries;
  • energy management strategies;
  • innovative aerodynamic concepts;
  • high-fidelity dynamic modelling;
  • fault-tolerant systemsdiagnostics and prognostics;
  • reliability/safety analysis.

Dr. Gianpietro Di Rito
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • full-electric/hybrid propulsion
  • power management optimization
  • innovative aerodynamics
  • digital twin
  • fault-tolerant systems
  • diagnostics and prognostics
  • reliability/safety analysis

Published Papers (1 paper)

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Research

18 pages, 5491 KiB  
Article
Condition Monitoring of the Torque Imbalance in a Dual-Stator Permanent Magnet Synchronous Motor for the Propulsion of a Lightweight Fixed-Wing UAV
by Aleksander Suti, Gianpietro Di Rito and Giuseppe Mattei
Drones 2023, 7(10), 618; https://doi.org/10.3390/drones7100618 - 3 Oct 2023
Cited by 1 | Viewed by 1855
Abstract
This paper deals with the development of a model-based technique to monitor the condition of torque imbalances in a dual-stator permanent magnet synchronous motor for UAV full-electric propulsion. Due to imperfections, degradations or uncertainties, the torque split between power lines can deviate from [...] Read more.
This paper deals with the development of a model-based technique to monitor the condition of torque imbalances in a dual-stator permanent magnet synchronous motor for UAV full-electric propulsion. Due to imperfections, degradations or uncertainties, the torque split between power lines can deviate from the design, causing internal force-fighting and reduced efficiency. This study demonstrates that, by only elaborating the measurements of speed and direct/quadrature currents of the stators during motor acceleration/deceleration, online estimations of demagnetization and electrical angle misalignment can be obtained, thus permitting the evaluation of the imbalance and total torque of the system. A relevant outcome is that the technique can be used for developing both signal-based and model-based monitoring schemes. Starting from physical first-principles, a nonlinear model of the propulsion system, including demagnetization and electrical angle misalignment, is developed in order to analytically derive the relationships between monitoring inputs (currents and speed) and outputs (degradations). The model is experimentally validated using a system prototype characterized by asymmetrical demagnetization and electrical angle misalignment. Finally, the monitoring effectiveness is assessed by simulating UAV flight manoeuvres with the experimentally validated model: injecting different levels of degradations and evaluating the torque imbalance. Full article
(This article belongs to the Special Issue Reliable and Green Long-Endurance Drones)
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